| 초록 |
Both farmers and consumers can benefit from the provision of more accurate price forecasting in unstable vegetable market. Focusing on the role of climate factors, which is expected to impact on vegetable price, this study develops price forecasting models for napa cabbage using Bayesian Structural Time Series (BSTS) and Vector Autoregressive (VAR) approaches. Across forecasting models, we show the consideration of climate factors enhances forecasting power significantly compared to pure time series models. BSTS and VAR can be used mutually complementarily byproviding various forecasting scenarios based on market conditions.
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